Nielsen Panels

For nearly 100 years, we’ve provided businesses around the world with critical insight into consumer behavior—and our panels make this possible.

What is a Panel?

A panel is a group of people that we’ve chosen to represent a larger universe of people. Since it’s not feasible to include everyone in a specific geographic area, like a country or city, we use sophisticated sampling and statistics to ensure that the sample we use is representative of the larger population. How do we do this? Our data scientists create miniature populations that mimic the behavior of the larger overall populations. In that way, we can accurately understand the behavior of a larger population without actually engaging with each and every person in that larger group.

How is panel data used?

We use panel data to understand consumer behavior. Whether it be to know what shows consumers are watching on TV or what they’re buying at the store, our panel data provides an accurate picture of how consumers are engaging with media and what products they’re buying. For example, the media industry considers our TV panel in the U.S. as the gold standard for knowing who’s watching what. In fact, our data is the currency that the TV industry uses for buying and selling advertising.

What is Representation?

It's impossible to engage with each and every person about their behavior—that's where data science comes in.

To start, we use large, statistically reliable data sets for a specific market. For example, think U.S. Census data, U.N. population stats for Melbourne, or government-issued population totals for the various suburbs within Shanghai. From there, we use random, probability-based sampling to isolate much smaller populations of the total. Once we have a smaller dataset, we use statistical modeling, weighting and other scientific techniques to ensure that the characteristics of the sample accurately reflect those of the larger population.

Using various data science techniques, we learn the behavior of a large population by isolating a representative sample and modeling it to mirror the behavior of the larger group.

Data Science & Big Data

Few people would argue that having less data is better than having more data. But not all data is equal. That doesn’t, however, mean that more data is automatically more useful. Every one of us is adding to our digital footprints every hour, and those footprints can help companies create content, products and experiences that are in line with your interests. But those footprints aren’t complete. Think about when you change the channel while you’re watching TV. The set-top box or content provider knows the channel changed and the address for the house that the TV is located in, but it doesn’t know WHO changed the channel.

That’s where data science comes into play, particularly in areas like digital, where technological measurement tools like code readers, meters and watermarks aren’t applicable. For example, an Internet provider knows if an online ad is clicked, but it doesn’t know who clicked it. But with the truth-set data from our panels as a foundation, we can use modeling and calibration techniques to gain an accurate representation of the behavior in the larger data set.